
"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class        _UpperCAmelCase(       __snake_case ,	unittest.TestCase      ):
		lowercase__         = DebertaTokenizer
		lowercase__         = True
		lowercase__         = DebertaTokenizerFast



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_UpperCamelCase									=    [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''[UNK]''',
							]
							_UpperCamelCase									=    dict(zip(__SCREAMING_SNAKE_CASE							,						range(len(__SCREAMING_SNAKE_CASE))))
							_UpperCamelCase									=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							_UpperCamelCase									=    {'''unk_token''': '''[UNK]'''}

							_UpperCamelCase									=    os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''vocab_file'''])
							_UpperCamelCase									=    os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['''merges_file'''])
							with open(self.vocab_file							,						'''w'''							,						encoding='''utf-8''') as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + '''\n''')
							with open(self.merges_file							,						'''w'''							,						encoding='''utf-8''') as fp:
												fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE))



		def 		UpperCAmelCase				(   self							,						**__a)			->			List[Any]:






							'''simple docstring'''
							kwargs.update(self.special_tokens_map)
							return self.tokenizer_class.from_pretrained(self.tmpdirname							,						**__SCREAMING_SNAKE_CASE)



		def 		UpperCAmelCase				(   self							,						__a)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    '''lower newer'''
							_UpperCamelCase									=    '''lower newer'''
							return input_text, output_text



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_tokenizer()
							_UpperCamelCase									=    '''lower newer'''
							_UpperCamelCase									=    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							_UpperCamelCase									=    tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE)

							_UpperCamelCase									=    tokens + [tokenizer.unk_token]
							_UpperCamelCase									=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)							,						__SCREAMING_SNAKE_CASE)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_tokenizer()
							_UpperCamelCase									=    tokenizer('''Hello'''							,						'''World''')
							_UpperCamelCase									=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
							self.assertListEqual(tokd['''token_type_ids''']							,						__SCREAMING_SNAKE_CASE)



		@slow
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.tokenizer_class.from_pretrained('''microsoft/deberta-base''')

							_UpperCamelCase									=    tokenizer.encode('''sequence builders'''							,						add_special_tokens=__SCREAMING_SNAKE_CASE)
							_UpperCamelCase									=    tokenizer.encode('''multi-sequence build'''							,						add_special_tokens=__SCREAMING_SNAKE_CASE)

							_UpperCamelCase									=    tokenizer.encode(
							    '''sequence builders'''							,						add_special_tokens=__SCREAMING_SNAKE_CASE							,						add_prefix_space=__SCREAMING_SNAKE_CASE)
							_UpperCamelCase									=    tokenizer.encode(
							    '''sequence builders'''							,						'''multi-sequence build'''							,						add_special_tokens=__SCREAMING_SNAKE_CASE							,						add_prefix_space=__SCREAMING_SNAKE_CASE)

							_UpperCamelCase									=    tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE)
							_UpperCamelCase									=    tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode




		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    [self.tokenizer_class]
							if self.test_rust_tokenizer:
												tokenizer_classes.append(self.rust_tokenizer_class)

							for tokenizer_class in tokenizer_classes:
												_UpperCamelCase									=    tokenizer_class.from_pretrained('''microsoft/deberta-base''')

												_UpperCamelCase									=    [
												    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
												    '''ALBERT incorporates two parameter reduction techniques''',
												    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
												    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
												    ''' vocabulary embedding.''',
												]

												_UpperCamelCase									=    tokenizer(__SCREAMING_SNAKE_CASE							,						padding=__SCREAMING_SNAKE_CASE)
												_UpperCamelCase									=    [tokenizer.decode(__SCREAMING_SNAKE_CASE							,						skip_special_tokens=__SCREAMING_SNAKE_CASE) for seq in encoding['''input_ids''']]

												# fmt: off
												_UpperCamelCase									=    {
												    '''input_ids''': [
												        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
												        [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
												        [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
												    ],
												    '''token_type_ids''': [
												        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
												        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
												        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
												    ],
												    '''attention_mask''': [
												        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
												        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
												        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
												    ]
												}
												# fmt: on

												_UpperCamelCase									=    [
												    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
												    '''ALBERT incorporates two parameter reduction techniques''',
												    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
												    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
												    ''' vocabulary embedding.''',
												]

												self.assertDictEqual(encoding.data							,						__SCREAMING_SNAKE_CASE)

												for expected, decoded in zip(__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE):
																	self.assertEqual(__SCREAMING_SNAKE_CASE							,						__SCREAMING_SNAKE_CASE)














"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		[
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)




